Contentbased Video Retrieval

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Content-based Video Retrieval

• Willem Jonker
• Philips Research / Twente University
• IFIP 2.6 meeting Lausanne May 15 17 2002

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Contents

• Image Retrieval Current Practice
• Content Based Video Retrieval
• Retrieving events from Tennis Video
• Retrieving events from Formula-1 Video

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Video Content Management
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Image Retrieval Current Practice
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Image Retrieval Current Practice
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Image Retrieval Current Practice
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Precision Recall
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Multi-level Modeling
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Features
background
composition
color
texture
face recognition
motion
text rec. OCR
shape
closed caption
berg-etappe etappe nr 17
speach rec.
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Query _at_ feature level
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QBIC
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Amore
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Video Retrieval
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Video Retrieval Modeling
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Video Retrieval Modeling
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Video Retrieval Modeling
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Video Retrieval Modeling
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Video Retrieval Query
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Video Retrieval Query
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Video Retrieval Query
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Video Retrieval Query
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The Semantic Gap
User
Concepts
Semantic gap
Raw Video Data
Data
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Tennis Goal
Video
Shot segmentation and classification
Shots (1) Playing (2) Close up
(3) Audience and other
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Tennis HMM History

• 50s - statisticians tried to characterize
  processes with incomplete observations.
• 60s - Baum et al. published HMM theory in a
  series of papers.
• 70s - HMMs were used at IBM and CMU for speech
  recognition.
• 90s - widespread applications (Gesture
  recognition human actions)

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Tennis HMM example
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Tennis HMM example
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Tennis Pre-processing
initial segmentation (robust M-estimator)
Step 1 Step 2 Step 3
locate player
fit 3D model
final segmentation
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Tennis Features
Shape Pie features Skeleton features
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Tennis HMM
Feature extraction
Time
Time
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Tennis HMM
. .
time
states
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Tennis Experiments

• Goals
• Determine the right feature set
• Investigate person independence
• Two series of experiments
• Same player in the training and evaluation
• Training with one player evaluation of strokes
  performed by various players
• Six events Forehand Backhand Service Smash
  Forehand volley and Backhand volley

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Tennis Experiment 1 results
Recognition results ()

• HMM parameters
• Codebook size of 24 symbols
• HMMs with 8 states
• Comparison to related work
• Improvement of 15
• TV videos

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Tennis System Screen Dump
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Tennis Some Performance Figures
Dual Pentium II 350 MHz Preprocessing 6.5 min.
per stroke of 45 frames initial segmentation
95sec fitting 3D models 150 sec final
segmentation 105 sec feature extraction 40
sec Training the model 20 sequences of 45
frames 50 iterations Baum-Welch 4 states 8
symbol codebook 12 sec 48 states 80 symbols 20
min. Inference between 1 and 30 sec depending
on model complexity in our case for 8 states
model with 20 symbols 2 sec
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Formula-1 Goal
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Formula-1 Bayesian Networks
A
B
C
D
E
P(XY) P(XY) P(Y) P(XY)P(Y)
P(YX)P(X) P(X1X2Xn) P i1..n P(XiPa(Xi))
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Formula-1 Audio BN
Kword Key Words PRate Pause Rate AV Average
Values DR Dynamic Range MV Maximum Values STE
Short Time Energy MFCC Mel-Frequency Cepstral
Coefficients
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Formula-1 Audio DBNs
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Formula-1 Effect of Audio DBNs

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Formula-1 Example Videos
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Formula-1 Highlights on Audio
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Formula-1 Combined Audio-Video
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Formula-1 Experimental Results
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Formula-1 System Screen Dump
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Formula-1 Some Performance Figures
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Bridging the Gap
User
Concepts
Domain knowledge
Domain features
Features
Raw Video Data
Data
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DMW projecthttp//wwwhome.cs.utwente.nl/dmw/
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Some Papers

• M. Petkovic W. Jonker Content-Based Video
  Retrieval by Integrating Spatio-Temporal and
  Stochastic Recognition of Events In Proc. IEEE
  International Workshop on Detection and
  Recognition of Events in Video Vancouver
  Canada July 2001.
• M.Petkovic W. Jonker Z. Zivkovic Recognizing
  Strokes in Tennis Videos Using Hidden Markov
  Models International Conference on
  Visualization Imaging and Image Processing
  Marbella Spain September 3-5 2001.
• H. E. Blok M. Windhouwer R. Zwol M. Petkovic
  P. M. G. Apers W. Jonker M. Kersten Flexible
  and Scalable Digital Library Search 27th
  International Conference on Very Large Databases
  Roma Italy September 11-14 2001.
• V. Mihajlovic M. Petkovic W. Jonker
  Content-Based Retrieval of TV Formula 1
  Programs 2nd International Workshop on
  Multimedia Data Document Engineering (MDDE02)
  March 24 2002 Prague